Rate Splitting Multiple Access-Enabled Adaptive Panoramic Video Semantic Transmission
In this paper, we propose an adaptive panoramic video semantic transmission (APVST) framework enabled by rate splitting multiple access (RSMA). The APVST framework consists of a semantic transmitter and receiver, utilizing a deep joint source-channel coding structure to adaptively extract and encode semantic features from panoramic frames. To achieve higher spectral efficiency and conserve bandwidth, APVST employs an entropy model and a dimension-adaptive module to control the transmission rate. Additionally, we take weighted-to-spherically-uniform peak signal-to-noise ratio (WS-PSNR) and weighted-to-spherically-uniform structural similarity (WS-SSIM) as distortion evaluation metrics for panoramic videos and design a weighted self-attention module for APVST. This module integrates weights and feature maps to enhance the quality of the immersive experience. Considering the overlap in the field of view when users watch panoramic videos, we further utilize RSMA to split the required panoramic video semantic streams into common and private messages for transmission. We propose an RSMA-enabled semantic stream transmission scheme and formulate a joint problem of latency and immersive experience quality by optimizing the allocation ratios of power, common rate, and channel bandwidth, aiming to maximize the quality of service (QoS) scores for users. To address the above problem, we propose a deep reinforcement learning algorithm based on proximal policy optimization (PPO) with high efficiency to handle dynamically changing environments. Simulation results demonstrate that our proposed APVST framework saves up to 20% and 50% of channel bandwidth compared to other semantic and traditional video transmission schemes, respectively. Moreover, our study confirms the efficiency of RSMA in panoramic video transmission, achieving performance gains of 13% and 20% compared to NOMA and OFDMA.
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Deep Reinforcement LearningSemantic CommunicationSSIMMethods 이 논문이 사용한 방법론
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